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所在平台: Udemy |
课程主页: https://www.udemy.com/course/unsupervised-machine-learning-arabic/
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课程名称：阿拉伯语无监督机器学习文凭 课程概述：本课程为使用Python的无监督机器学习文凭，旨在丰富阿拉伯地区人工智能领域的内容。它是一门全面的培训课程，基于互动、应用、详细的解释以及从基础到深入理解算法的全过程。课程强调代码中的实际应用，并建立强大的模型，以应对真实场景中的问题。适合初学者、专业人士以及任何对数据科学、数据分析、机器学习和人工智能感兴趣的人群，包括数据分析师、数据科学家、机器学习工程师和人工智能工程师。该文凭使您能掌握无监督机器学习和数据科学，不仅通过编码，还通过对与算法相关的数学有扎实的理解，提供理论与实践的详细讲解。 学习内容： - 课程介绍：无监督机器学习简介和基本原理 - 线性与非线性降维 - 主成分分析（PCA） - 增量主成分分析（IPCA） - 核主成分分析（Kernel PCA） - 奇异值分解（SVD） - 高斯与稀疏随机投影 - Isomap算法 - 局部线性嵌入（LLE） - t-SNE算法 - 关于使用降维方法进行异常检测的实际项目 - 聚类分析介绍 - K均值算法及其用例 - 使用K均值的图像分割 - 使用K均值的数据预处理 - 基于K均值的半监督机器学习 - DBSCAN算法 - 层次聚类算法 - 高斯混合模型（GMM）算法 - 使用不同聚类技术进行群体分割的实际项目 无论你是人工智能爱好者、开发者还是数据科学家，这门课程将赋予你在无监督机器学习及其在人工智能现实应用方面所需的知识和实践技能。快来加入我们，踏上这段丰富的学习旅程，迈向掌握无监督机器学习，为前沿AI项目做好准备。
Diploma in Unsupervised Machine Learning Using Python. It is a unique diploma that enriches Arabic content in the field of artificial intelligence. It is a comprehensive training course based on interaction, application, detailed explanation, and a thorough breakdown of algorithms from scratch to an excellent understanding of the algorithm. The course emphasizes practical application in coding and building a strong model used in real-life scenarios. Suitable for beginners, professionals, and anyone interested in data science, data analysis, machine learning, and artificial intelligence, including Data Analysts, Data Scientists, Machine Learning Engineers, and AI Engineers.The diploma qualifies you to master unsupervised machine learning and data science not only through coding but also through a solid understanding of the mathematics related to algorithms, with detailed explanations from both theoretical and practical perspectives._________________________________________________________________________________________What You Will Learn:Introduction to the Course:Introduction to Unsupervised Machine LearningUnderstanding the fundamentals of unsupervised machine learning.Linear and Nonlinear Dimensionality ReductionPrincipal Component Analysis (PCA)Incremental Principal Component Analysis (IPCA)Kernel Principal Component Analysis (Kernel PCA)Singular Value Decomposition (SVD)Gaussian & Sparse Random ProjectionIsomap AlgorithmLocally Linear Embedding (LLE)t-SNE AlgorithmPractical Project on Anomaly Detection Using Dimensionality Reduction MethodsIntroduction to ClusteringK-Means AlgorithmUse Cases of K-MeansImage Segmentation using K-MeansData Preprocessing using K-MeansSemi-supervised ML using K-MeansDBSCAN AlgorithmHierarchical Clustering AlgorithmGaussian Mixture Models (GMM) AlgorithmPractical Project on Group Segmentation Using Different Techniques of Clustering_________________________________________________________________________________________Whether you're an AI enthusiast, developer, or data scientist, this course will empower you with the knowledge and practical skills necessary to excel in unsupervised Machine Learning and its applications in real life of AI.Join us now and embark on an enriching learning journey that will set you on the path to mastering Unsupervised Machine Learning for cutting-edge AI projects.
